[{"_1":2,"_302":-5,"_303":-5},"loaderData",{"_3":4,"_26":27,"_28":29,"_30":31,"_240":241},"root",{"_5":6,"_14":15},"toasts",[7],{"_8":9,"_10":11,"_12":13},"uid","81469ea1-802c-4f51-aedc-3e30192dc91d","title","This MCP server does not support inspection","type","error","visitor",{"_16":17,"_18":19},"viewportWidth",1280,"visitorSession",{"_20":21,"_23":-5,"_8":24,"_25":-5},"attributes",[22],"BOT","membership","KcwcVA8ELQ","userAccount","routes/_public/_layout",{},"routes/_public/mcp/servers/~namespace/_layout",{},"routes/_public/mcp/servers/~namespace/~slug/_pages/_layout",{"_32":-5,"_33":34,"_35":36,"_161":162},"githubUser","maintainer",false,"mcpServer",{"_37":38,"_39":40,"_20":69,"_72":73,"_74":73,"_75":76,"_81":82,"_103":-5,"_104":105,"_106":34,"_107":108,"_91":132,"_133":96,"_134":-5,"_135":-5,"_136":137,"_138":139,"_146":-5,"_147":148,"_153":-5,"_115":92,"_154":155,"_157":-5,"_8":158,"_159":160},"addedAt",["D",1742307677710],"argumentsJsonSchema",{"_41":42,"_66":67,"_12":68},"properties",{"_43":44,"_50":51,"_54":55,"_58":59,"_62":63},"HOST",{"_45":46,"_47":48,"_12":49},"default","0.0.0.0","description","Server host address","string","PORT",{"_45":52,"_47":53,"_12":49},"8000","Server port number","LOG_LEVEL",{"_45":56,"_47":57,"_12":49},"DEBUG","Logging configuration level","CORS_ORIGINS",{"_45":60,"_47":61,"_12":49},"*","CORS configuration for allowed origins","MAX_MEMORIES",{"_45":64,"_47":65,"_12":49},"0","Maximum number of memories to store (0 for unlimited)","required",[],"object",[70,71],"language:python","license_grade:f","descriptionMarkdown","Model Context Protocol (MCP) server implementation for semantic search and memory management using TxtAI. This server provides a robust API for storing, retrieving, and managing text-based memories with semantic search capabilities. You can use Claude and Cline AI Also","descriptionPlainText","githubProject",{"_77":78,"_79":80},"readme","# TxtAI Assistant MCP\n\nA Model Context Protocol (MCP) server implementation for semantic search and memory management using [txtai](https://github.com/neuml/txtai). This server provides a robust API for storing, retrieving, and managing text-based memories with semantic search capabilities.\n\n## About txtai\n\nThis project is built on top of [txtai](https://github.com/neuml/txtai), an excellent open-source AI-powered search engine created by [NeuML](https://github.com/neuml). txtai provides:\n\n- 🔍 All-in-one semantic search solution\n- 🧠 Neural search with transformers\n- 💡 Zero-shot text classification\n- 🔄 Text extraction and embeddings\n- 🌐 Multi-language support\n- 🚀 High performance and scalability\n\nWe extend txtai's capabilities by integrating it with the Model Context Protocol (MCP), enabling AI assistants like Claude and Cline to leverage its powerful semantic search capabilities. Special thanks to the txtai team for creating such a powerful and flexible tool.\n\n## Features\n\n- 🔍 Semantic search across stored memories\n- 💾 Persistent storage with file-based backend\n- 🏷️ Tag-based memory organization and retrieval\n- 📊 Memory statistics and health monitoring\n- 🔄 Automatic data persistence\n- 📝 Comprehensive logging\n- 🔒 Configurable CORS settings\n- 🤖 Integration with Claude and Cline AI\n\n## Prerequisites\n\n- Python 3.8 or higher\n- pip (Python package installer)\n- virtualenv (recommended)\n\n## Installation\n\n1. Clone this repository:\n```bash\ngit clone https://github.com/yourusername/txtai-assistant-mcp.git\ncd txtai-assistant-mcp\n```\n\n2. Run the start script:\n```bash\n./scripts/start.sh\n```\n\nThe script will:\n- Create a virtual environment\n- Install required dependencies\n- Set up necessary directories\n- Create a configuration file from template\n- Start the server\n\n## Configuration\n\nThe server can be configured using environment variables in the `.env` file. A template is provided at `.env.template`:\n\n```ini\n# Server Configuration\nHOST=0.0.0.0\nPORT=8000\n\n# CORS Configuration\nCORS_ORIGINS=*\n\n# Logging Configuration\nLOG_LEVEL=DEBUG\n\n# Memory Configuration\nMAX_MEMORIES=0\n```\n\n## Integration with Claude and Cline AI\n\nThis TxtAI Assistant can be used as an MCP server with Claude and Cline AI to enhance their capabilities with semantic memory and search functionality.\n\n### Configuration for Claude\n\nTo use this server with Claude, add it to Claude's MCP configuration file (typically located at `~/Library/Application Support/Claude/claude_desktop_config.json` on macOS):\n\n```json\n{\n \"mcpServers\": {\n \"txtai-assistant\": {\n \"command\": \"path/to/txtai-assistant-mcp/scripts/start.sh\",\n \"env\": {}\n }\n }\n}\n```\n\n### Configuration for Cline\n\nTo use with Cline, add the server configuration to Cline's MCP settings file (typically located at `~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json`):\n\n```json\n{\n \"mcpServers\": {\n \"txtai-assistant\": {\n \"command\": \"path/to/txtai-assistant-mcp/scripts/start.sh\",\n \"env\": {}\n }\n }\n}\n```\n\n### Available MCP Tools\n\nOnce configured, the following tools become available to Claude and Cline:\n\n1. `store_memory`: Store new memory content with metadata and tags\n```json\n{\n \"content\": \"Memory content to store\",\n \"metadata\": {\n \"source\": \"conversation\",\n \"timestamp\": \"2023-01-01T00:00:00Z\"\n },\n \"tags\": [\"important\", \"context\"],\n \"type\": \"conversation\"\n}\n```\n\n2. `retrieve_memory`: Retrieve memories based on semantic search\n```json\n{\n \"query\": \"search query\",\n \"n_results\": 5\n}\n```\n\n3. `search_by_tag`: Search memories by tags\n```json\n{\n \"tags\": [\"important\", \"context\"]\n}\n```\n\n4. `delete_memory`: Delete a specific memory by content hash\n```json\n{\n \"content_hash\": \"hash_value\"\n}\n```\n\n5. `get_stats`: Get database statistics\n```json\n{}\n```\n\n6. `check_health`: Check database and embedding model health\n```json\n{}\n```\n\n### Usage Examples\n\nIn Claude or Cline, you can use these tools through the MCP protocol:\n\n```python\n# Store a memory\n\ntxtai-assistant\nstore_memory\n\n{\n \"content\": \"Important information to remember\",\n \"tags\": [\"important\"]\n}\n\n\n\n# Retrieve memories\n\ntxtai-assistant\nretrieve_memory\n\n{\n \"query\": \"what was the important information?\",\n \"n_results\": 5\n}\n\n\n```\n\nThe AI will automatically use these tools to maintain context and retrieve relevant information during conversations.\n\n## API Endpoints\n\n### Store Memory\n```http\nPOST /store\n```\nStore a new memory with optional metadata and tags.\n\n**Request Body:**\n```json\n{\n \"content\": \"Memory content to store\",\n \"metadata\": {\n \"source\": \"example\",\n \"timestamp\": \"2023-01-01T00:00:00Z\"\n },\n \"tags\": [\"example\", \"memory\"],\n \"type\": \"general\"\n}\n```\n\n### Search Memories\n```http\nPOST /search\n```\nSearch memories using semantic search.\n\n**Request Body:**\n```json\n{\n \"query\": \"search query\",\n \"n_results\": 5,\n \"similarity_threshold\": 0.7\n}\n```\n\n### Search by Tags\n```http\nPOST /search_tags\n```\nSearch memories by tags.\n\n**Request Body:**\n```json\n{\n \"tags\": [\"example\", \"memory\"]\n}\n```\n\n### Delete Memory\n```http\nDELETE /memory/{content_hash}\n```\nDelete a specific memory by its content hash.\n\n### Get Statistics\n```http\nGET /stats\n```\nGet system statistics including memory counts and tag distribution.\n\n### Health Check\n```http\nGET /health\n```\nCheck the health status of the server.\n\n## Directory Structure\n\n```\ntxtai-assistant-mcp/\n├── server/\n│ ├── main.py # Main server implementation\n│ └── requirements.txt # Python dependencies\n├── scripts/\n│ └── start.sh # Server startup script\n├── data/ # Data storage directory\n├── logs/ # Log files directory\n├── .env.template # Environment configuration template\n└── README.md # This file\n```\n\n## Data Storage\n\nMemories and tags are stored in JSON files in the `data` directory:\n- `memories.json`: Contains all stored memories\n- `tags.json`: Contains the tag index\n\n## Logging\n\nLogs are stored in the `logs` directory. The default log file is `server.log`.\n\n## Development\n\nTo contribute to this project:\n\n1. Fork the repository\n2. Create a feature branch\n3. Make your changes\n4. Submit a pull request\n\n## Error Handling\n\nThe server implements comprehensive error handling:\n- Invalid requests return appropriate HTTP status codes\n- Errors are logged with stack traces\n- User-friendly error messages are returned in responses\n\n## Security Considerations\n\n- CORS settings are configurable via environment variables\n- File paths are sanitized to prevent directory traversal\n- Input validation is performed on all endpoints\n\n## License\n\nThis project is licensed under the MIT License - see the LICENSE file for details.\n\n## Contributing\n\nContributions are welcome! 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